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MicroRNA Cancer Regulation

Overview of attention for book
Cover of 'MicroRNA Cancer Regulation'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 MicroRNAs in Human Cancer.
  3. Altmetric Badge
    Chapter 2 Bioinformatics, Non-coding RNAs and Its Possible Application in Personalized Medicine
  4. Altmetric Badge
    Chapter 3 MicroRNA Target Prediction and Validation
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    Chapter 4 MicroRNA-Regulated Networks: The Perfect Storm for Classical Molecular Biology, the Ideal Scenario for Systems Biology
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    Chapter 5 The p53/microRNA Network in Cancer: Experimental and Bioinformatics Approaches
  7. Altmetric Badge
    Chapter 6 MicroRNAs in Melanoma Biology.
  8. Altmetric Badge
    Chapter 7 MicroRNAs in the Lung
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    Chapter 8 The E2F1-miRNA Cancer Progression Network
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    Chapter 9 Modeling microRNA-Transcription Factor Networks in Cancer
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    Chapter 10 Coordinated Networks of microRNAs and Transcription Factors with Evolutionary Perspectives
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    Chapter 11 Mathematical modeling of microRNA-mediated mechanisms of translation repression
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    Chapter 12 Web Resources for microRNA Research
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    Chapter 13 Discovery of microRNA Regulatory Networks by Integrating Multidimensional High-Throughput Data.
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    Chapter 14 Discovering Functional microRNA-mRNA Regulatory Modules in Heterogeneous Data
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    Chapter 15 Elucidating the Role of microRNAs in Cancer Through Data Mining Techniques
  17. Altmetric Badge
    Chapter 16 Working Together: Combinatorial Regulation by microRNAs
  18. Altmetric Badge
    Chapter 17 Erratum
Attention for Chapter 11: Mathematical modeling of microRNA-mediated mechanisms of translation repression
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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1 news outlet
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1 Google+ user

Citations

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27 Mendeley
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Chapter title
Mathematical modeling of microRNA-mediated mechanisms of translation repression
Chapter number 11
Book title
MicroRNA Cancer Regulation
Published in
Advances in experimental medicine and biology, December 2012
DOI 10.1007/978-94-007-5590-1_11
Pubmed ID
Book ISBNs
978-9-40-075589-5, 978-9-40-075590-1
Authors

A. Zinovyev, N. Morozova, A. N. Gorban, A. Harel-Belan, Andrei Zinovyev, Nadya Morozova, Alexander N. Gorban, Annick Harel-Belan

Editors

Ulf Schmitz, Olaf Wolkenhauer, Julio Vera

Abstract

MicroRNAs can affect the protein translation using nine mechanistically different mechanisms, including repression of initiation and degradation of the transcript. There is a hot debate in the current literature about which mechanism and in which situations has a dominant role in living cells. The worst, same experimental systems dealing with the same pairs of mRNA and miRNA can provide ambiguous evidences about which is the actual mechanism of translation repression observed in the experiment. We start with reviewing the current knowledge of various mechanisms of miRNA action and suggest that mathematical modeling can help resolving some of the controversial interpretations. We describe three simple mathematical models of miRNA translation that can be used as tools in interpreting the experimental data on the dynamics of protein synthesis. The most complex model developed by us includes all known mechanisms of miRNA action. It allowed us to study possible dynamical patterns corresponding to different miRNA-mediated mechanisms of translation repression and to suggest concrete recipes on determining the dominant mechanism of miRNA action in the form of kinetic signatures. Using computational experiments and systematizing existing evidences from the literature, we justify a hypothesis about co-existence of distinct miRNA-mediated mechanisms of translation repression. The actually observed mechanism will be that acting on or changing the sensitive parameters of the translation process. The limiting place can vary from one experimental setting to another. This model explains the majority of existing controversies reported.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 7%
Mexico 1 4%
Portugal 1 4%
Poland 1 4%
Unknown 22 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 30%
Student > Ph. D. Student 6 22%
Student > Bachelor 3 11%
Student > Doctoral Student 2 7%
Student > Postgraduate 2 7%
Other 4 15%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 52%
Biochemistry, Genetics and Molecular Biology 2 7%
Computer Science 2 7%
Medicine and Dentistry 2 7%
Neuroscience 1 4%
Other 2 7%
Unknown 4 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 August 2012.
All research outputs
#2,060,187
of 22,673,450 outputs
Outputs from Advances in experimental medicine and biology
#287
of 4,904 outputs
Outputs of similar age
#21,230
of 280,069 outputs
Outputs of similar age from Advances in experimental medicine and biology
#14
of 157 outputs
Altmetric has tracked 22,673,450 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,904 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,069 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 157 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.